Digital Twin Simulation
Validate AI agents in a risk-free environment before production deployment. Build team confidence through hands-on experience with autonomous planning.
The Beer Game
The MIT Beer Game is the world's most widely used supply chain simulation. It demonstrates how rational individual decisions create irrational system behavior — the bullwhip effect — where demand variability amplifies as it moves upstream.
Autonomy includes a full digital implementation with multi-echelon supply chain simulation (Retailer, Wholesaler, Distributor, Factory), supporting 2-8 participants per scenario in real-time via WebSocket.
Mixed Human-AI Scenarios
The real power is in mixed scenarios. Place your team alongside AI agents to see the difference in real-time:
- Human vs Human — Classic Beer Game for bullwhip education
- Human vs AI — Your team competes against AI agents to see autonomous performance
- Human + AI — Team up with AI agents in mixed roles to build collaboration skills
- AI vs AI — Benchmark different agent strategies against each other
Four Use Cases
1. Employee Training
Scenario-based learning achieves 3-5x higher engagement than traditional supply chain training. New hires experience the bullwhip effect firsthand, understand why autonomous planning matters, and learn to work with AI agents before touching production systems.
2. Agent Validation
Before deploying agents to production, run them through simulation scenarios that stress-test edge cases: demand spikes, supplier disruptions, capacity constraints, quality holds. Verify agent behavior in a risk-free environment with full observability.
3. Confidence Building
Human vs AI competitions demonstrate AI effectiveness in a way that dashboards and reports cannot. When planners see agents consistently outperform them on cost and service level in the simulation, trust in autonomous decisions follows.
4. Continuous Improvement
Human decisions in simulation generate training data for AI agents. Override patterns reveal where human judgment adds value, feeding back into the agent training pipeline for continuous improvement.
Digital Twin Training Pipeline
The simulation module serves as the digital twin for the five-phase cold-start pipeline:
- Individual BC warm-start — Behavioral cloning from expert curricula
- Coordinated multi-agent traces — SimPy simulation generates training data
- Stochastic stress-testing — Monte Carlo scenarios validate robustness
- Copilot calibration — Human overrides during copilot mode fine-tune agents
- Autonomous CDC relearning — Production outcomes drive continuous improvement